Skip to main content
. 2018 Nov 30;35(14):2371–2379. doi: 10.1093/bioinformatics/bty991

Table 1.

Error rate comparison between RF, HSVM, Omni-PolyA and our model (DeeReCT-PolyA) on the Dragon human poly(A) data

Variants Size Error Rate (%)
RF HSVM Omni- PolyA DeeReCT- PolyA Rel
AATAAA 5190 20.06 18.59 14.02 11.81 2.21
ATTAAA 2400 18.42 16.21 12.50 9.00 3.50
AAAAAG 1250 16.64 9.36 10.80 5.77 3.59
AAGAAA 1230 11.06 5.45 4.87 7.76 −2.89
TATAAA 880 19.55 15.34 13.52 7.69 5.83
AATACA 780 19.36 11.15 13.85 10.45 0.70
AGTAAA 690 27.83 16.96 14.49 9.55 4.94
ACTAAA 670 22.09 14.33 13.13 10.72 2.41
GATAAA 460 20.00 9.57 8.48 8.04 0.44
CATAAA 410 18.54 9.27 13.41 9.02 0.25
AATATA 410 24.88 12.68 14.39 8.78 3.90
AATAGA 370 18.38 5.14 11.62 4.59 0.55
Average 19.19 14.42 12.43 9.57 2.86

Note: Rel denotes the improvement of DeeReCT-PolyA with respect to the best of the other three methods. Bold indicates the error rate of the best model for each PAS motif variant. Average is the weighted average of all motif variants with the size as weights. While results of all three previous methods are reported for 12 variant-specific models, the results of DeeReCT-PolyA are the performance of one single generic model that deals with all 12 variants.